The Journal of Ambient Intelligence and Smart Environments (JAISE) serves as a forum to discuss the latest developments on Ambient Intelligence (AmI) and Smart Environments (SmE). Given the multi-disciplinary nature of the areas involved, the journal aims to promote participation from several different communities covering topics ranging from enabling technologies such as multi-modal sensing and vision processing, to algorithmic aspects in interpretive and reasoning domains, to application-oriented efforts in human-centered services, as well as contributions from the fields of robotics, networking, HCI, mobile, collaborative and pervasive computing. This diversity stems from the fact that smart environments can be defined with a variety of different characteristics based on the applications they serve, their interaction models with humans, the practical system design aspects, as well as the multi-faceted conceptual and algorithmic considerations that would enable them to operate seamlessly and unobtrusively.

The Journal of Ambient Intelligence and Smart Environments will focus on both the technical and application aspects of these.

Abstract: Ambient intelligence has a history of focusing on technologies that are integrated into a person's environment. However, ambient intelligence can be found on a person's body as well. In this thematic issue we examine the role of wearable computing in the field of ambient intelligence. In this article we provide an overview of the field of wearable computing and discuss its relationship to the fields of smart environments and ambient intelligence. In addition, we introduce the papers presented in the thematic issue highlighting a number of research projects which are defining the state of the art in wearable computing and…ambient intelligence.
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Abstract: Continuous miniaturization of electronics and sensing elements stimulate the evolution of novel unobtrusively integrated smart garments that sense their environment and provide personalized assistance to its wearer. The development of smart garments requires robust integration techniques for electronics and textiles in one common system. Furthermore, recognition algorithms are needed to derive information on the wearer's activity and context within the smart garment. In this work both challenges are addressed in a smart shirt system, called SMASH. SMASH was developed as a rapid prototyping system for smart garment developments. We introduced in this work our approach for prototyping smart garments and…present design, implementation, and evaluation of SMASH. The SMASH system embeds a distributed hierarchical architecture of sensing and processing functions in an off-the-shelf long-sleeve shirt. The system design focused on scalability regarding sensors and processing resources, as well as rapid deployment in different applications. We demonstrated the versatility of SMASH in three application evaluations that represent different prototyping phases of smart garments. For these studies several systems of different sizes were implemented. The SMASH system helps to bypass time- and cost-intensive implementation iterations using multiple garment prototypes.
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Abstract: We propose a distributed recognition framework to classify continuous human actions using a low-bandwidth wearable motion sensor network, called distributed sparsity classifier (DSC). The algorithm classifies human actions using a set of training motion sequences as prior examples. It is also capable of rejecting outlying actions that are not in the training categories. The classification is operated in a distributed fashion on individual sensor nodes and a base station computer. We model the distribution of multiple action classes as a mixture subspace model, one subspace for each action class. Given a new test sample, we seek the sparsest linear representation…of the sample w.r.t. all training examples. We show that the dominant coefficients in the representation only correspond to the action class of the test sample, and hence its membership is encoded in the sparse representation. Fast linear solvers are provided to compute such representation via ℓ1 -minimization. To validate the accuracy of the framework, a public wearable action recognition database is constructed, called wearable action recognition database (WARD). The database is comprised of 20 human subjects in 13 action categories. Using up to five motion sensors in the WARD database, DSC achieves state-of-the-art performance. We further show that the recognition precision only decreases gracefully using smaller subsets of active sensors. It validates the robustness of the distributed recognition framework on an unreliable wireless network. It also demonstrates the ability of DSC to conserve sensor energy for communication while preserve accurate global classification.
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Abstract: Advancements in computing and communications technologies are enabling information processing on the go. The rapid proliferation of sensors in various applications is a result of miniaturization and advancements in embedded systems technologies. Textiles are pervasive, customizable and familiar to users. Together, they all present a powerful framework – in the form of a wearable sensor network – for harnessing ambient intelligence in various domains ranging from healthcare to entertainment. The need for personalized mobile information processing is established using typical scenarios and the building blocks of an ambient intelligent system are presented. The importance of a platform for creating the…wearable sensor network is discussed. Then, the dual roles of textiles as both a physical and an information infrastructure for the wearable sensor network are explored from the viewpoints of human factors and technology features. The realization of a fabric-based wearable sensor network is illustrated with the wearable motherboard, or smart shirt. Finally, the challenges and opportunities for furthering the wearable sensor network paradigm – from both technical and market acceptance viewpoints – for harnessing ambient intelligence are presented.
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Abstract: Ambient intelligence is a way of subtly gathering information from an environment and acting on it. In the field of physiological monitoring, there are several goals that ambient intelligence can help us achieve. First, when patients are anxious, unobtrusive monitoring does not aggravate their anxiety. Second, when patients are at risk and there are insufficient caregivers to attend to each patient individually in a timely manner, unobtrusive pervasive monitoring can reassure patients that they are being cared for. Furthermore, caregivers appreciate being able to monitor more patients. The SMART system was developed to monitor patients' vital signs and locations in…the waiting area of a hospital's emergency department. This paper reviews the SMART system and compares it to several other systems in related areas.
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Abstract: Accelerometer based activity recognition systems have typically focused on recognizing simple ambulatory activities of daily life, such as walking, sitting, standing, climbing stairs, etc. In this work, we developed and evaluated algorithms for detecting and recognizing short duration hand movements (lift to mouth, scoop, stir, pour, unscrew cap). These actions are a part of the larger and complex Instrumental Activities of Daily Life (IADL) making a drink and drinking. We collected data using small wireless tri-axial accelerometers worn simultaneously on different parts of the hand. Acceleration data for training was collected from 5 subjects, who also performed the two IADLs…without being given specific instructions on how to complete them. Feature vectors (mean, variance, correlation, spectral entropy and spectral energy) were calculated and tested on three classifiers (AdaBoost, HMM, k-NN). AdaBoost showed the best performance, with an overall accuracy of 86% for detecting each of these hand actions. The results show that although some actions are recognized well with the generalized classifer trained on the subject-independent data, other actions require some amount of subject-specific training. We also observed an improvement in the performance of the system when multiple accelerometers placed on the right hand were used.
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Abstract: In this article we introduce the analysis of eye motion as a new input modality for activity recognition, context-awareness and mobile HCI applications. We describe a novel embedded eye tracker that, in contrast to common systems using video cameras, relies on Electrooculography (EOG). This self-contained wearable device consists of goggles with dry electrodes integrated into the frame and a small pocket-worn component with a DSP for real-time EOG signal processing. It can store data locally for long-term recordings or stream processed EOG signals to a remote device over Bluetooth. We show how challenges associated with wearability, eye motion analysis and…signal artefacts caused by physical activity can be addressed with a combination of a special mechanical design, optimised algorithms for eye movement detection and adaptive signal processing. In two case studies, we demonstrate that EOG is a suitable measurement technique for the recognition of reading activity and eye-based human-computer interaction. Eventually, wearable EOG goggles may pave the way for seamless eye movement analysis in everyday environments and new forms of context-awareness not possible today.
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Abstract: Athletes in any sports can greatly benefit from feedback systems for improving the quality of their training. In this paper, we present a golf swing training system which incorporates wearable motion sensors to obtain inertial information and provide feedback on the quality of movements. The sensors are placed on a golf club and athlete's body at positions which capture the unique movements of a golf swing. We introduce a quantitative model which takes into consideration signal processing techniques on the collected data and quantifies the correctness of the performed actions. We evaluate the effectiveness of our framework on data obtained…from four subjects and discuss ongoing research.
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Abstract: Continuous monitoring of health parameters is crucial for preterm new born babies admitted at the neonatal intensive care unit (NICU) in hospitals. The critically ill neonates are extremely tiny and vulnerable to external disturbance. In the context of ambient intelligence and smart environments, non-invasive health monitoring with wearable sensors is promising for the survival of these neonates and the quality of their life later on. A key question for health monitoring with wearable sensors is how to obtain reliable electrical power for the sensors, signal amplifiers, filters and transmitters. In this paper, we propose a design of wireless power supply…based on the principle of inductive contactless energy transfer for use in NICU. The design process consists of scientific and user research, idea generation and selection, proof of technology, prototype implementation, and experimental validation. The proposed power supply satisfies the requirements of neonatal monitoring and provides continuous power when the neonate is inside the incubator or during Kangaroo mother care. A prototype is designed and implemented to demonstrate the performance of the power supply and the possibilities for aesthetic features. Experimental results show that the prototype transfers approximately 840 mW of power.
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